This inventor holds 1 USPTO granted patent. Top assignee: The Mathworks, Inc.. Active years: 2021.
Company Filing History:
Years Active: 2021
Title: Vibha Patil: Innovator in Deep Learning Technologies
Introduction
Vibha Patil is a prominent inventor based in Hyderabad, Telangana, India. She has made significant contributions to the field of deep learning through her innovative patent. Her work focuses on enhancing the efficiency of programmable logic devices for deep learning networks.
Latest Patents
Vibha holds a patent titled "Systems and methods for configuring programmable logic devices for deep learning networks." This patent outlines systems and methods that configure a programmable logic device to efficiently run a deep learning (DL) network. The architecture code and algorithmic code generated by this system define convolutional and fully connected processor cores structured to run the layers of a Deep Neural Network (DNN). The processor cores are interconnected by a First In First Out (FIFO) memory, and the architecture code also defines stride-efficient memories for implementing convolution. The algorithmic code includes configuration instructions for running the DNN's layers at the processor cores, as well as a schedule for executing these instructions, moving network parameters, and transferring outputs between the layers. Vibha has 1 patent to her name.
Career Highlights
Vibha Patil is currently employed at The MathWorks, Inc., where she continues to develop innovative solutions in the field of deep learning. Her expertise and contributions have positioned her as a key figure in her organization.
Collaborations
Vibha has collaborated with notable colleagues such as Wang Chen and Yongfeng Gu, further enhancing her work in the field of deep learning technologies.
Conclusion
Vibha Patil is a trailblazer in the realm of deep learning, with her innovative patent showcasing her commitment to advancing technology. Her contributions are paving the way for future developments in programmable logic devices and deep learning networks.
